Bio Med CentralPage 1 of 2 page number not for citation purposes BMC Neuroscience Open Access Poster presentation A neural field model for spatio-temporal brain activity using a morphol
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BMC Neuroscience
Open Access
Poster presentation
A neural field model for spatio-temporal brain activity using a
morphological model of cortical connectivity
Address: 1 Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany and 2 Institute for Biomedical Enginnering and
Informatics, Technical University Ilmenau, Germany
Email: Manh Nguyen Trong* - nguyen@cbs.mpg.de
* Corresponding author
Background
Electroencephalography and magnetoencephalography
(EEG and MEG) are brain signals with high temporal
res-olutions and are believed to reflect neural mass action For
modeling the neuronal structures, which are responsible
for the generation of EEG/MEG, one can use so-called
neural mass models, like the one of Jansen and Rit [1] In
such models, a brain area (e.g a cortical column) is
mod-eled by two or three neural masses subsuming similar
cells, which are characterized by a single input-output
relationship It turns out that this type of model is too
simple to reproduce the entire richness of typical EEG
spectra We therefore propose to use neural field models
[2], which take into account the spatial dimension of
active brain areas and describe the use of realistic local
connectivity information in these models
Methods
Dendritic and axonal arborizations need to be modeled
for a formalized description of the connectivity between
neurons and neural masses The complex structure of
these arborizations is represented with the help of
trivari-ate Gaussian distributions (Figure 1) The number of the
synaptic contacts will be weighted with the probability of
synaptic connection and the gain of average postsynaptic
potential The neural field model is an extension to the
neural mass model The space in the model of the neural
field, e.g a cortical area or the entire cortex, could be
defined as a homogeneous continuum of different neural
masses with different structural properties Interactions
among neural masses of the neural field model can be described by a system of second order integro-differential equations and an embedded connectivity matrix In the neural field model, not only the functions (excitatory and
from Eighteenth Annual Computational Neuroscience Meeting: CNS*2009
Berlin, Germany 18–23 July 2009
Published: 13 July 2009
BMC Neuroscience 2009, 10(Suppl 1):P287 doi:10.1186/1471-2202-10-S1-P287
<supplement> <title> <p>Eighteenth Annual Computational Neuroscience Meeting: CNS*2009</p> </title> <editor>Don H Johnson</editor> <note>Meeting abstracts – A single PDF containing all abstracts in this Supplement is available <a href="http://www.biomedcentral.com/content/files/pdf/1471-2202-10-S1-full.pdf">here</a>.</note> <url>http://www.biomedcentral.com/content/pdf/1471-2202-10-S1-info.pdf</url> </supplement>
This abstract is available from: http://www.biomedcentral.com/1471-2202/10/S1/P287
© 2009 Trong et al; licensee BioMed Central Ltd
Geometry of synaptic arbors
Figure 1 Geometry of synaptic arbors.
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inhibitory) but also the morphology (pyramidal and
non-pyramidal) of the neurons will be taken into
considera-tion
Results
The neural field model could produce a large variety of
EEG-like waveforms and rhythms In addition, this model
is able to generate signals of multiple independent
fre-quencies and spatiotemporal activity pattern (Figure 2)
We propose a new formalism to model neural fields and
describe the incorporation of precise local connectivity
information into these models Our model is capable of
producing output with very EEG-like time courses and
spectra Our results might constitute an important step on
the road towards a universal model for neuronal mass
action
References
1. Jansen BH, Rit VG: Electroencephalogram and visual evoked
potential generation in a mathematical model of coupled
cortical columns Biol Cybern 1995, 73:357-366.
2. Grimbert F: PhD thesis University of Nice-Sophia Antipolis; 2008
Spatiotemporal activity modeled pattern by the neural field
model
Figure 2
Spatiotemporal activity modeled pattern by the
neu-ral field model.